Typically, there exist three classes of descriptors to represent a point cloud: bag-of-words, hand-craft global descriptor and and deep-learning-based global descriptor. Bag-of-words (BoW) [1]} was adopted in LIDAR settings due to its success in image retrieval. BoW ﬁrst calculates local descriptors of key points, and vector-quantizes the local descriptors into words of a dictionary that has been constructed off line. The descriptor uses the histogram of the words, which has high complexity due to vector quantization or clustering. In addition to bag-of-words, there are several hand-crafted descriptors such as Viewpoint Feature Histogram (VFH) [2]}, Small-Sized Signatures (Z-projection) [3]} and Ensemble of Shape Functions (ESF) [4]}. Compared with the BoW method and its variants, hand-crafted global descriptors avoid the time-consuming local key point detection part. However, global descriptors are sometimes sensitive to laser sensor orientation and position changes. This is due to the lack of global structure and local distribution in building such descriptors.
